In today's changing, increasingly competitive health care environment, medical clinics are under new pressures to measure and control costs of delivered health care services. These new processes are loosely referred to as treatment outcome measurements. A component of this new era of outcomes measurement, or practice management, is the evaluation of a patient's perspective of the outcome of the delivered services.
In the new medical clinic setting, client satisfaction data is increasingly being considered as an important data point in deciding how to tune the delivery of a specific treatment plan. This client satisfaction information is collated with billing and resource utilization data to modify treatment plans to achieve optimal results with respect to goals of finance, clinic visits, and patient quality of life issues.
Conventional methods of collecting data relating to the health status and treatment satisfaction of an individual has been by using pen and pencil on a paper-based questionnaire. This method is time intensive for both the data collector and the individual, i.e., the health clinic staff data collector and the individual patient. Inputting such data into a database involves significant staff time and jeopardizes the integrity of the input data because human error is introduced during the transcription process. Some commercially available products have improved upon the pencil and paper approach and have sought to impact the speed of this data collection by using computerized forms and scanning the client responses into the computer (see, for example, DocuMed and OrthoGraphics). These currently commercially available methods and systems are time intensive for the individual and for the data collector. The scanning methods also jeopardize the integrity of the data because mistakes in interpretation by the scanning technology are common.
Direct individual-computer interactive programs have been researched with favorable results. The time taken by the data collector are significantly reduced and the integrity of the data generally is maintained. However, the methods employed by these approaches have required the individual to look at a question and then make a response by touching generically labelled control buttons. In addition, such an approach uses a small, dedicated liquid crystal display that is extremely difficult to read by the individual (see, Rozen et al., Medical Care, Vol. 30(5), pp. MS74-MS84, 1992).
Other methods and systems involve technology that displays the questions for the individual on the computer display and requires the user to use a computer keyboard to enter responses. The obvious problem with such an approach is that to the computer-illiterate individual, or to those individual user who are only marginally computer-literate, the computer keyboard is a formidable obstacle to completion of an entire questionnaire. Such an obstacle significantly affects the integrity and reliability of the collected data. (See, e.g., Maitland et al., Archives of Physical Medicine and Rehab., Vol. 75, pp. 639-642, 1994.)
Yet other methods use a computer mouse system that an individual user must use to enter responses to the questionnaire. Such a system is next to impossible to use for someone who has not had extensive training or at least exposure to such computer technology. Using a mouse requires some dexterity and eye-to-hand coordination that may be difficult for some users.
All of the existing systems have significant drawbacks that, in one way or another, impact the ability of the system to procure valuable, reliable data from an individual user. Thus, there remains a need for a system that brings client questionnaires on-line to a computer, while maintaining the integrity of the collected data, that requires as little supervision as possible, and that provides an interactive and user-friendly interface as possible.
As a corollary to enhanced data collection capabilities, there is an increasing demand for enhanced data analysis, preferably in real-time. While systems and agencies presently exist which are capable of collecting patient data, such systems and agencies rely on manual collection techniques and semi-manual data analyses. For example, the American Academy of Orthopedic Surgeons (Rosemont, Ill.), recently issued a standard questionnaire to be used by practitioners to collect patient data. The data was manually collected, and then the collected data was sent back to the AAOS, in hand-written form, where the data was entered into a database. From the database, certain statistics regarding each particular patient was generated. Clearly, the data collection and analysis were not performed contemporaneously in that instance. However, the methodology used by the AAOS to collect and analyze patient data exemplifies the present state of the art.
Traditional statistical analysis techniques applied to patient data analyzes the data collected for an individual patient against historical data for that patient. The ability to compare data for an individual against a group would provide useful information for hospitals, insurance companies, and individual health care providers. For example, the progress of an individual patient over the course of a particular treatment, the responsiveness of a particular patient to a therapy, or the recovery rate of a particular patient, all as tracked against group norms, is useful for physical therapists, physicians, and insurance agents. If such analyses can be performed relatively contemporaneously with therapy or treatment, adjustments may be made during the course of the therapy or treatment to optimize effectiveness. Using currently available systems, there is a significant delay between data entry and obtaining the results of such analysis, if such analysis is even made available to a user.
Data collection methodologies currently in practice among hospitals, clinics, and the like, include either walking a patient through a series of questions as the system user enters the patient responses, or having the patient manually complete a paper copy questionnaire. In both instances, patient data is entered by a party other than the patient. Such systems potentially introduce transcription or other third party errors, making the data inherently suspect.
The same issues discussed above in connection with healthcare organizations exist in a variety of other organizations, such as automobile, transportation, and any service organizations. In such organizations, it may be desirable to identify group trends and individual activity compared against such group trends.
Accordingly, there remains a need for a computer system that allows a patient or user to directly enter data into the database for real-time analyses based on group, and not only on individual, dynamics. There further remains a need for a computer system that provides access to sufficient group data to enable essentially real-time correlations and statistics to be performed using demographic information to generate outcomes profile reports that may be used to analyze, inter alia, individual/patient satisfaction with particular treatments, in a health-care environment.